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Robust Merging of Information

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  • Henrique de Oliveira
  • Yuhta Ishii
  • Xiao Lin

Abstract

When multiple sources of information are available, any decision must take into account their correlation. If information about this correlation is lacking, an agent may find it desirable to make a decision that is robust to possible correlations. Our main results characterize the strategies that are robust to possible hidden correlations. In particular, with two states and two actions, the robustly optimal strategy pays attention to a single information source, ignoring all others. More generally, the robustly optimal strategy may need to combine multiple information sources, but can be constructed quite simply by using a decomposition of the original problem into separate decision problems, each requiring attention to only one information source. An implication is that an information source generates value to the agent if and only if it is best for at least one of these decomposed problems.

Suggested Citation

  • Henrique de Oliveira & Yuhta Ishii & Xiao Lin, 2021. "Robust Merging of Information," Papers 2106.00088, arXiv.org.
    • Handle: RePEc:arx:papers:2106.00088
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    References listed on IDEAS

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    2. Itai Arieli & Yakov Babichenko & Rann Smorodinsky, 2018. "Robust forecast aggregation," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(52), pages 12135-12143, December.
    3. Gabriel Carroll, 2017. "Robustness and Separation in Multidimensional Screening," Econometrica, Econometric Society, vol. 85, pages 453-488, March.
    4. Itai Areili & Yakov Babichenko & Rann Smorodinsky, 2017. "Robust Forecast Aggregation," Papers 1710.02838, arXiv.org, revised Feb 2018.
    5. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    6. de Oliveira, Henrique, 2018. "Blackwell's informativeness theorem using diagrams," Games and Economic Behavior, Elsevier, vol. 109(C), pages 126-131.
    7. Giuseppe Moscarini & Lones Smith, 2002. "The Law of Large Demand for Information," Econometrica, Econometric Society, vol. 70(6), pages 2351-2366, November.
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